






from ....configuration_utils import PretrainedConfig
from ....utils import logging


__A					=	logging.get_logger(__name__)

# TODO: upload to AWS
__A					=	{
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}



class 				_A			(     UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							Any				= 'retribert'



    def __init__(				self      :     Dict ,     __SCREAMING_SNAKE_CASE      :     int=30522 ,     __SCREAMING_SNAKE_CASE      :     List[Any]=768 ,     __SCREAMING_SNAKE_CASE      :     Any=8 ,     __SCREAMING_SNAKE_CASE      :     Dict=12 ,     __SCREAMING_SNAKE_CASE      :     List[str]=3072 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]="gelu" ,     __SCREAMING_SNAKE_CASE      :     List[Any]=0.1 ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=0.1 ,     __SCREAMING_SNAKE_CASE      :     Dict=512 ,     __SCREAMING_SNAKE_CASE      :     List[str]=2 ,     __SCREAMING_SNAKE_CASE      :     List[Any]=0.02 ,     __SCREAMING_SNAKE_CASE      :     Any=1e-12 ,     __SCREAMING_SNAKE_CASE      :     Tuple=True ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=128 ,     __SCREAMING_SNAKE_CASE      :     str=0 ,     **__SCREAMING_SNAKE_CASE      :     List[Any] ,     )      ->							Optional[Any]:
           super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=vocab_size
           __UpperCAmelCase     				=hidden_size
           __UpperCAmelCase     				=num_hidden_layers
           __UpperCAmelCase     				=num_attention_heads
           __UpperCAmelCase     				=hidden_act
           __UpperCAmelCase     				=intermediate_size
           __UpperCAmelCase     				=hidden_dropout_prob
           __UpperCAmelCase     				=attention_probs_dropout_prob
           __UpperCAmelCase     				=max_position_embeddings
           __UpperCAmelCase     				=type_vocab_size
           __UpperCAmelCase     				=initializer_range
           __UpperCAmelCase     				=layer_norm_eps
           __UpperCAmelCase     				=share_encoders
           __UpperCAmelCase     				=projection_dim











"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   =  {
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RobertaPreLayerNormForCausalLM",
           "RobertaPreLayerNormForMaskedLM",
           "RobertaPreLayerNormForMultipleChoice",
           "RobertaPreLayerNormForQuestionAnswering",
           "RobertaPreLayerNormForSequenceClassification",
           "RobertaPreLayerNormForTokenClassification",
           "RobertaPreLayerNormModel",
           "RobertaPreLayerNormPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFRobertaPreLayerNormForCausalLM",
           "TFRobertaPreLayerNormForMaskedLM",
           "TFRobertaPreLayerNormForMultipleChoice",
           "TFRobertaPreLayerNormForQuestionAnswering",
           "TFRobertaPreLayerNormForSequenceClassification",
           "TFRobertaPreLayerNormForTokenClassification",
           "TFRobertaPreLayerNormMainLayer",
           "TFRobertaPreLayerNormModel",
           "TFRobertaPreLayerNormPreTrainedModel",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "FlaxRobertaPreLayerNormForCausalLM",
           "FlaxRobertaPreLayerNormForMaskedLM",
           "FlaxRobertaPreLayerNormForMultipleChoice",
           "FlaxRobertaPreLayerNormForQuestionAnswering",
           "FlaxRobertaPreLayerNormForSequenceClassification",
           "FlaxRobertaPreLayerNormForTokenClassification",
           "FlaxRobertaPreLayerNormModel",
           "FlaxRobertaPreLayerNormPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_roberta_prelayernorm import (
           ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
           RobertaPreLayerNormConfig,
           RobertaPreLayerNormOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta_prelayernorm import (
                  ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaPreLayerNormForCausalLM,
                  RobertaPreLayerNormForMaskedLM,
                  RobertaPreLayerNormForMultipleChoice,
                  RobertaPreLayerNormForQuestionAnswering,
                  RobertaPreLayerNormForSequenceClassification,
                  RobertaPreLayerNormForTokenClassification,
                  RobertaPreLayerNormModel,
                  RobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta_prelayernorm import (
                  TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaPreLayerNormForCausalLM,
                  TFRobertaPreLayerNormForMaskedLM,
                  TFRobertaPreLayerNormForMultipleChoice,
                  TFRobertaPreLayerNormForQuestionAnswering,
                  TFRobertaPreLayerNormForSequenceClassification,
                  TFRobertaPreLayerNormForTokenClassification,
                  TFRobertaPreLayerNormMainLayer,
                  TFRobertaPreLayerNormModel,
                  TFRobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta_prelayernorm import (
                  FlaxRobertaPreLayerNormForCausalLM,
                  FlaxRobertaPreLayerNormForMaskedLM,
                  FlaxRobertaPreLayerNormForMultipleChoice,
                  FlaxRobertaPreLayerNormForQuestionAnswering,
                  FlaxRobertaPreLayerNormForSequenceClassification,
                  FlaxRobertaPreLayerNormForTokenClassification,
                  FlaxRobertaPreLayerNormModel,
                  FlaxRobertaPreLayerNormPreTrainedModel,
              )

else:
       import sys

       SCREAMING_SNAKE_CASE__   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






